Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations5783
Missing cells18804
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory216.0 B

Variable types

Numeric2
Text13
Categorical6
DateTime6

Alerts

Phases is highly overall correlated with Study TypeHigh correlation
Study Type is highly overall correlated with PhasesHigh correlation
Study Results is highly imbalanced (94.5%)Imbalance
Gender is highly imbalanced (84.3%)Imbalance
Funded Bys is highly imbalanced (72.7%)Imbalance
Study Type is highly imbalanced (67.1%)Imbalance
Acronym has 3303 (57.1%) missing valuesMissing
Interventions has 886 (15.3%) missing valuesMissing
Phases has 2461 (42.6%) missing valuesMissing
Results First Posted has 5747 (99.4%) missing valuesMissing
Locations has 585 (10.1%) missing valuesMissing
Study Documents has 5601 (96.9%) missing valuesMissing
Enrollment is highly skewed (γ1 = 34.06593382)Skewed
Rank is uniformly distributedUniform
Rank has unique valuesUnique
NCT Number has unique valuesUnique
URL has unique valuesUnique
Enrollment has 107 (1.9%) zerosZeros

Reproduction

Analysis started2024-12-09 14:00:10.539822
Analysis finished2024-12-09 14:00:18.694001
Duration8.15 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Rank
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct5783
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2892
Minimum1
Maximum5783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:18.831917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile290.1
Q11446.5
median2892
Q34337.5
95-th percentile5493.9
Maximum5783
Range5782
Interquartile range (IQR)2891

Descriptive statistics

Standard deviation1669.5526
Coefficient of variation (CV)0.57730036
Kurtosis-1.2
Mean2892
Median Absolute Deviation (MAD)1446
Skewness0
Sum16724436
Variance2787406
MonotonicityStrictly increasing
2024-12-09T19:30:19.023144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3864 1
 
< 0.1%
3862 1
 
< 0.1%
3861 1
 
< 0.1%
3860 1
 
< 0.1%
3859 1
 
< 0.1%
3858 1
 
< 0.1%
3857 1
 
< 0.1%
3856 1
 
< 0.1%
3855 1
 
< 0.1%
Other values (5773) 5773
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5783 1
< 0.1%
5782 1
< 0.1%
5781 1
< 0.1%
5780 1
< 0.1%
5779 1
< 0.1%
5778 1
< 0.1%
5777 1
< 0.1%
5776 1
< 0.1%
5775 1
< 0.1%
5774 1
< 0.1%

NCT Number
Text

UNIQUE 

Distinct5783
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:19.387443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters63613
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5783 ?
Unique (%)100.0%

Sample

1st rowNCT04785898
2nd rowNCT04595136
3rd rowNCT04395482
4th rowNCT04416061
5th rowNCT04395924
ValueCountFrequency (%)
nct04785898 1
 
< 0.1%
nct04473170 1
 
< 0.1%
nct04416061 1
 
< 0.1%
nct04395924 1
 
< 0.1%
nct04516954 1
 
< 0.1%
nct04476940 1
 
< 0.1%
nct04634214 1
 
< 0.1%
nct04602884 1
 
< 0.1%
nct04384588 1
 
< 0.1%
nct04355897 1
 
< 0.1%
Other values (5773) 5773
99.8%
2024-12-09T19:30:19.901019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 10040
15.8%
0 8659
13.6%
N 5783
9.1%
C 5783
9.1%
T 5783
9.1%
3 4654
7.3%
5 3741
 
5.9%
6 3687
 
5.8%
7 3569
 
5.6%
8 3245
 
5.1%
Other values (3) 8669
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 10040
15.8%
0 8659
13.6%
N 5783
9.1%
C 5783
9.1%
T 5783
9.1%
3 4654
7.3%
5 3741
 
5.9%
6 3687
 
5.8%
7 3569
 
5.6%
8 3245
 
5.1%
Other values (3) 8669
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 10040
15.8%
0 8659
13.6%
N 5783
9.1%
C 5783
9.1%
T 5783
9.1%
3 4654
7.3%
5 3741
 
5.9%
6 3687
 
5.8%
7 3569
 
5.6%
8 3245
 
5.1%
Other values (3) 8669
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 10040
15.8%
0 8659
13.6%
N 5783
9.1%
C 5783
9.1%
T 5783
9.1%
3 4654
7.3%
5 3741
 
5.9%
6 3687
 
5.8%
7 3569
 
5.6%
8 3245
 
5.1%
Other values (3) 8669
13.6%

Title
Text

Distinct5775
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:20.457038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length286
Median length183
Mean length80.203701
Min length18

Characters and Unicode

Total characters463818
Distinct characters113
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5767 ?
Unique (%)99.7%

Sample

1st rowDiagnostic Performance of the ID Nowâ„¢ COVID-19 Screening Test Versus Simplexaâ„¢ COVID-19 Direct Assay
2nd rowStudy to Evaluate the Efficacy of COVID19-0001-USR in Patients With Mild/or Moderate COVID-19 Infection in Outpatient
3rd rowLung CT Scan Analysis of SARS-CoV2 Induced Lung Injury
4th rowThe Role of a Private Hospital in Hong Kong Amid COVID-19 Pandemic
5th rowMaternal-foetal Transmission of SARS-Cov-2
ValueCountFrequency (%)
covid-19 4427
 
7.0%
of 4038
 
6.4%
in 3162
 
5.0%
and 2184
 
3.5%
the 1841
 
2.9%
patients 1628
 
2.6%
with 1480
 
2.4%
for 1333
 
2.1%
to 999
 
1.6%
study 942
 
1.5%
Other values (7256) 40908
65.0%
2024-12-09T19:30:20.935728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57159
 
12.3%
e 33925
 
7.3%
i 31441
 
6.8%
t 28661
 
6.2%
n 28599
 
6.2%
a 26418
 
5.7%
o 26098
 
5.6%
r 18371
 
4.0%
s 16737
 
3.6%
l 12504
 
2.7%
Other values (103) 183905
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 463818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
57159
 
12.3%
e 33925
 
7.3%
i 31441
 
6.8%
t 28661
 
6.2%
n 28599
 
6.2%
a 26418
 
5.7%
o 26098
 
5.6%
r 18371
 
4.0%
s 16737
 
3.6%
l 12504
 
2.7%
Other values (103) 183905
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 463818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
57159
 
12.3%
e 33925
 
7.3%
i 31441
 
6.8%
t 28661
 
6.2%
n 28599
 
6.2%
a 26418
 
5.7%
o 26098
 
5.6%
r 18371
 
4.0%
s 16737
 
3.6%
l 12504
 
2.7%
Other values (103) 183905
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 463818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
57159
 
12.3%
e 33925
 
7.3%
i 31441
 
6.8%
t 28661
 
6.2%
n 28599
 
6.2%
a 26418
 
5.7%
o 26098
 
5.6%
r 18371
 
4.0%
s 16737
 
3.6%
l 12504
 
2.7%
Other values (103) 183905
39.7%

Acronym
Text

MISSING 

Distinct2338
Distinct (%)94.3%
Missing3303
Missing (%)57.1%
Memory size45.3 KiB
2024-12-09T19:30:21.220828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length8.3794355
Min length2

Characters and Unicode

Total characters20781
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2271 ?
Unique (%)91.6%

Sample

1st rowCOVID-IDNow
2nd rowCOVID-19
3rd rowTAC-COVID19
4th rowCOVID-19
5th rowTMF-COVID-19
ValueCountFrequency (%)
covid-19 76
 
2.9%
covid 32
 
1.2%
corona 11
 
0.4%
covid19 9
 
0.3%
recover 8
 
0.3%
protect 7
 
0.3%
scope 6
 
0.2%
2 6
 
0.2%
hope 5
 
0.2%
cover 5
 
0.2%
Other values (2328) 2440
93.7%
2024-12-09T19:30:21.684286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2297
 
11.1%
O 1879
 
9.0%
I 1653
 
8.0%
V 1252
 
6.0%
D 1077
 
5.2%
A 1030
 
5.0%
- 1018
 
4.9%
E 995
 
4.8%
R 911
 
4.4%
S 790
 
3.8%
Other values (71) 7879
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2297
 
11.1%
O 1879
 
9.0%
I 1653
 
8.0%
V 1252
 
6.0%
D 1077
 
5.2%
A 1030
 
5.0%
- 1018
 
4.9%
E 995
 
4.8%
R 911
 
4.4%
S 790
 
3.8%
Other values (71) 7879
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2297
 
11.1%
O 1879
 
9.0%
I 1653
 
8.0%
V 1252
 
6.0%
D 1077
 
5.2%
A 1030
 
5.0%
- 1018
 
4.9%
E 995
 
4.8%
R 911
 
4.4%
S 790
 
3.8%
Other values (71) 7879
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2297
 
11.1%
O 1879
 
9.0%
I 1653
 
8.0%
V 1252
 
6.0%
D 1077
 
5.2%
A 1030
 
5.0%
- 1018
 
4.9%
E 995
 
4.8%
R 911
 
4.4%
S 790
 
3.8%
Other values (71) 7879
37.9%

Status
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
Recruiting
2805 
Completed
1025 
Not yet recruiting
1004 
Active, not recruiting
526 
Enrolling by invitation
 
181
Other values (7)
 
242

Length

Max length25
Median length23
Mean length12.708975
Min length9

Characters and Unicode

Total characters73496
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowActive, not recruiting
2nd rowNot yet recruiting
3rd rowRecruiting
4th rowActive, not recruiting
5th rowRecruiting

Common Values

ValueCountFrequency (%)
Recruiting 2805
48.5%
Completed 1025
 
17.7%
Not yet recruiting 1004
 
17.4%
Active, not recruiting 526
 
9.1%
Enrolling by invitation 181
 
3.1%
Withdrawn 107
 
1.9%
Terminated 74
 
1.3%
Suspended 27
 
0.5%
Available 19
 
0.3%
No longer available 12
 
0.2%
Other values (2) 3
 
0.1%

Length

2024-12-09T19:30:21.847884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
recruiting 4335
46.9%
not 1531
 
16.6%
completed 1025
 
11.1%
yet 1004
 
10.9%
active 526
 
5.7%
enrolling 181
 
2.0%
by 181
 
2.0%
invitation 181
 
2.0%
withdrawn 107
 
1.2%
terminated 74
 
0.8%
Other values (8) 90
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 10136
13.8%
t 8966
12.2%
e 8166
11.1%
r 6247
8.5%
n 5808
7.9%
c 4861
 
6.6%
g 4530
 
6.2%
u 4362
 
5.9%
3452
 
4.7%
o 2947
 
4.0%
Other values (22) 14021
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 10136
13.8%
t 8966
12.2%
e 8166
11.1%
r 6247
8.5%
n 5808
7.9%
c 4861
 
6.6%
g 4530
 
6.2%
u 4362
 
5.9%
3452
 
4.7%
o 2947
 
4.0%
Other values (22) 14021
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 10136
13.8%
t 8966
12.2%
e 8166
11.1%
r 6247
8.5%
n 5808
7.9%
c 4861
 
6.6%
g 4530
 
6.2%
u 4362
 
5.9%
3452
 
4.7%
o 2947
 
4.0%
Other values (22) 14021
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 10136
13.8%
t 8966
12.2%
e 8166
11.1%
r 6247
8.5%
n 5808
7.9%
c 4861
 
6.6%
g 4530
 
6.2%
u 4362
 
5.9%
3452
 
4.7%
o 2947
 
4.0%
Other values (22) 14021
19.1%

Study Results
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
No Results Available
5747 
Has Results
 
36

Length

Max length20
Median length20
Mean length19.943974
Min length11

Characters and Unicode

Total characters115336
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Results Available
2nd rowNo Results Available
3rd rowNo Results Available
4th rowNo Results Available
5th rowNo Results Available

Common Values

ValueCountFrequency (%)
No Results Available 5747
99.4%
Has Results 36
 
0.6%

Length

2024-12-09T19:30:21.977033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T19:30:22.102823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
results 5783
33.4%
no 5747
33.2%
available 5747
33.2%
has 36
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 17277
15.0%
s 11602
10.1%
11530
10.0%
e 11530
10.0%
a 11530
10.0%
R 5783
 
5.0%
u 5783
 
5.0%
t 5783
 
5.0%
N 5747
 
5.0%
o 5747
 
5.0%
Other values (5) 23024
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 17277
15.0%
s 11602
10.1%
11530
10.0%
e 11530
10.0%
a 11530
10.0%
R 5783
 
5.0%
u 5783
 
5.0%
t 5783
 
5.0%
N 5747
 
5.0%
o 5747
 
5.0%
Other values (5) 23024
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 17277
15.0%
s 11602
10.1%
11530
10.0%
e 11530
10.0%
a 11530
10.0%
R 5783
 
5.0%
u 5783
 
5.0%
t 5783
 
5.0%
N 5747
 
5.0%
o 5747
 
5.0%
Other values (5) 23024
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 17277
15.0%
s 11602
10.1%
11530
10.0%
e 11530
10.0%
a 11530
10.0%
R 5783
 
5.0%
u 5783
 
5.0%
t 5783
 
5.0%
N 5747
 
5.0%
o 5747
 
5.0%
Other values (5) 23024
20.0%
Distinct3067
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:22.347528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length604
Median length288
Mean length30.595193
Min length3

Characters and Unicode

Total characters176932
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2766 ?
Unique (%)47.8%

Sample

1st rowCovid19
2nd rowSARS-CoV-2 Infection
3rd rowcovid19
4th rowCOVID
5th rowMaternal Fetal Infection Transmission|COVID-19|SARS-CoV 2
ValueCountFrequency (%)
covid-19 1166
 
7.4%
covid19 753
 
4.8%
infection 495
 
3.2%
respiratory 390
 
2.5%
coronavirus 357
 
2.3%
virus 245
 
1.6%
acute 241
 
1.5%
syndrome 221
 
1.4%
disease 220
 
1.4%
covid 201
 
1.3%
Other values (4600) 11393
72.7%
2024-12-09T19:30:22.782045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 12718
 
7.2%
e 12549
 
7.1%
o 12066
 
6.8%
9899
 
5.6%
n 9595
 
5.4%
r 9056
 
5.1%
a 8799
 
5.0%
s 8136
 
4.6%
t 7823
 
4.4%
C 6927
 
3.9%
Other values (69) 79364
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 12718
 
7.2%
e 12549
 
7.1%
o 12066
 
6.8%
9899
 
5.6%
n 9595
 
5.4%
r 9056
 
5.1%
a 8799
 
5.0%
s 8136
 
4.6%
t 7823
 
4.4%
C 6927
 
3.9%
Other values (69) 79364
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 12718
 
7.2%
e 12549
 
7.1%
o 12066
 
6.8%
9899
 
5.6%
n 9595
 
5.4%
r 9056
 
5.1%
a 8799
 
5.0%
s 8136
 
4.6%
t 7823
 
4.4%
C 6927
 
3.9%
Other values (69) 79364
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 12718
 
7.2%
e 12549
 
7.1%
o 12066
 
6.8%
9899
 
5.6%
n 9595
 
5.4%
r 9056
 
5.1%
a 8799
 
5.0%
s 8136
 
4.6%
t 7823
 
4.4%
C 6927
 
3.9%
Other values (69) 79364
44.9%

Interventions
Text

MISSING 

Distinct4337
Distinct (%)88.6%
Missing886
Missing (%)15.3%
Memory size45.3 KiB
2024-12-09T19:30:23.074702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length1279
Median length336
Mean length55.768838
Min length9

Characters and Unicode

Total characters273100
Distinct characters111
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4076 ?
Unique (%)83.2%

Sample

1st rowDiagnostic Test: ID Nowâ„¢ COVID-19 Screening Test
2nd rowDrug: Drug COVID19-0001-USR|Drug: normal saline
3rd rowOther: Lung CT scan analysis in COVID-19 patients
4th rowDiagnostic Test: COVID 19 Diagnostic Test
5th rowDiagnostic Test: Diagnosis of SARS-Cov2 by RT-PCR and : IgG, Ig M serologies in the amniotoc fluid, the blood cord and the placenta
ValueCountFrequency (%)
drug 1547
 
5.0%
other 1179
 
3.8%
test 836
 
2.7%
placebo 798
 
2.6%
biological 635
 
2.1%
of 573
 
1.9%
diagnostic 532
 
1.7%
behavioral 513
 
1.7%
and 402
 
1.3%
device 338
 
1.1%
Other values (6737) 23417
76.1%
2024-12-09T19:30:23.557157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25873
 
9.5%
e 21134
 
7.7%
i 18148
 
6.6%
a 17284
 
6.3%
o 16654
 
6.1%
r 15540
 
5.7%
t 14734
 
5.4%
n 13009
 
4.8%
l 11043
 
4.0%
s 9097
 
3.3%
Other values (101) 110584
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 273100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25873
 
9.5%
e 21134
 
7.7%
i 18148
 
6.6%
a 17284
 
6.3%
o 16654
 
6.1%
r 15540
 
5.7%
t 14734
 
5.4%
n 13009
 
4.8%
l 11043
 
4.0%
s 9097
 
3.3%
Other values (101) 110584
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 273100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25873
 
9.5%
e 21134
 
7.7%
i 18148
 
6.6%
a 17284
 
6.3%
o 16654
 
6.1%
r 15540
 
5.7%
t 14734
 
5.4%
n 13009
 
4.8%
l 11043
 
4.0%
s 9097
 
3.3%
Other values (101) 110584
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 273100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25873
 
9.5%
e 21134
 
7.7%
i 18148
 
6.6%
a 17284
 
6.3%
o 16654
 
6.1%
r 15540
 
5.7%
t 14734
 
5.4%
n 13009
 
4.8%
l 11043
 
4.0%
s 9097
 
3.3%
Other values (101) 110584
40.5%
Distinct5687
Distinct (%)98.9%
Missing35
Missing (%)0.6%
Memory size45.3 KiB
2024-12-09T19:30:23.928324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11709
Median length1385
Mean length369.49304
Min length3

Characters and Unicode

Total characters2123846
Distinct characters145
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5637 ?
Unique (%)98.1%

Sample

1st rowEvaluate the diagnostic performance of the ID Now â„¢ COVID-19 test carried out by nurses in an emergency department in comparison with the reference PCR test: Simplexa â„¢ COVID-19 Direct
2nd rowChange on viral load results from baseline after using COVID19-0001-USR via nebulization
3rd rowA qualitative analysis of parenchymal lung damage induced by COVID-19|A quantitative analysis of parenchymal lung damage induced by COVID-19|The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.|Automated segmentation of lung scans of patients with COVID-19 and ARDS.|Knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques.|The ability within which the analysis of artificial intelligence that uses deep learning models can be used to predict clinical outcomes
4th rowProportion of asymptomatic subjects|Proportion of subjects with recent contact history|Proportion of subjects with recent travel history
5th rowCOVID-19 by positive PCR in cord blood and / or positive serologies|COVID-19 by positive PCR in amniotic fluid and placenta|New born infected by COVID-19
ValueCountFrequency (%)
of 21816
 
8.2%
in 8367
 
3.1%
the 7507
 
2.8%
and 5884
 
2.2%
to 5032
 
1.9%
with 3481
 
1.3%
covid-19 2558
 
1.0%
from 2527
 
0.9%
at 2503
 
0.9%
by 2215
 
0.8%
Other values (32481) 204515
76.8%
2024-12-09T19:30:24.519530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260657
 
12.3%
e 189729
 
8.9%
i 145165
 
6.8%
t 141306
 
6.7%
a 137378
 
6.5%
o 131711
 
6.2%
n 130531
 
6.1%
s 102395
 
4.8%
r 101829
 
4.8%
l 63975
 
3.0%
Other values (135) 719170
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2123846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
260657
 
12.3%
e 189729
 
8.9%
i 145165
 
6.8%
t 141306
 
6.7%
a 137378
 
6.5%
o 131711
 
6.2%
n 130531
 
6.1%
s 102395
 
4.8%
r 101829
 
4.8%
l 63975
 
3.0%
Other values (135) 719170
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2123846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
260657
 
12.3%
e 189729
 
8.9%
i 145165
 
6.8%
t 141306
 
6.7%
a 137378
 
6.5%
o 131711
 
6.2%
n 130531
 
6.1%
s 102395
 
4.8%
r 101829
 
4.8%
l 63975
 
3.0%
Other values (135) 719170
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2123846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
260657
 
12.3%
e 189729
 
8.9%
i 145165
 
6.8%
t 141306
 
6.7%
a 137378
 
6.5%
o 131711
 
6.2%
n 130531
 
6.1%
s 102395
 
4.8%
r 101829
 
4.8%
l 63975
 
3.0%
Other values (135) 719170
33.9%
Distinct3631
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:24.776786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3268
Median length588
Mean length61.998962
Min length5

Characters and Unicode

Total characters358540
Distinct characters135
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2937 ?
Unique (%)50.8%

Sample

1st rowGroupe Hospitalier Paris Saint Joseph
2nd rowUnited Medical Specialties
3rd rowUniversity of Milano Bicocca
4th rowHong Kong Sanatorium & Hospital
5th rowCentre Hospitalier Régional d'Orléans|Centre de Biophysique Moléculaire - Pr Chantal Pichon|Professeur TOUMI Hechmi
ValueCountFrequency (%)
university 2520
 
6.1%
of 2438
 
5.9%
hospital 1441
 
3.5%
de 1246
 
3.0%
and 757
 
1.8%
medical 667
 
1.6%
health 662
 
1.6%
research 634
 
1.5%
institute 527
 
1.3%
center 371
 
0.9%
Other values (9432) 29901
72.6%
2024-12-09T19:30:25.224569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35381
 
9.9%
i 30587
 
8.5%
e 30199
 
8.4%
a 26112
 
7.3%
n 22908
 
6.4%
t 21619
 
6.0%
o 18857
 
5.3%
r 18208
 
5.1%
s 17607
 
4.9%
l 14264
 
4.0%
Other values (125) 122798
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35381
 
9.9%
i 30587
 
8.5%
e 30199
 
8.4%
a 26112
 
7.3%
n 22908
 
6.4%
t 21619
 
6.0%
o 18857
 
5.3%
r 18208
 
5.1%
s 17607
 
4.9%
l 14264
 
4.0%
Other values (125) 122798
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35381
 
9.9%
i 30587
 
8.5%
e 30199
 
8.4%
a 26112
 
7.3%
n 22908
 
6.4%
t 21619
 
6.0%
o 18857
 
5.3%
r 18208
 
5.1%
s 17607
 
4.9%
l 14264
 
4.0%
Other values (125) 122798
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35381
 
9.9%
i 30587
 
8.5%
e 30199
 
8.4%
a 26112
 
7.3%
n 22908
 
6.4%
t 21619
 
6.0%
o 18857
 
5.3%
r 18208
 
5.1%
s 17607
 
4.9%
l 14264
 
4.0%
Other values (125) 122798
34.2%

Gender
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing10
Missing (%)0.2%
Memory size45.3 KiB
All
5567 
Female
 
162
Male
 
44

Length

Max length6
Median length3
Mean length3.0918067
Min length3

Characters and Unicode

Total characters17849
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowFemale

Common Values

ValueCountFrequency (%)
All 5567
96.3%
Female 162
 
2.8%
Male 44
 
0.8%
(Missing) 10
 
0.2%

Length

2024-12-09T19:30:25.371799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T19:30:25.487577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
all 5567
96.4%
female 162
 
2.8%
male 44
 
0.8%

Most occurring characters

ValueCountFrequency (%)
l 11340
63.5%
A 5567
31.2%
e 368
 
2.1%
a 206
 
1.2%
F 162
 
0.9%
m 162
 
0.9%
M 44
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 11340
63.5%
A 5567
31.2%
e 368
 
2.1%
a 206
 
1.2%
F 162
 
0.9%
m 162
 
0.9%
M 44
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 11340
63.5%
A 5567
31.2%
e 368
 
2.1%
a 206
 
1.2%
F 162
 
0.9%
m 162
 
0.9%
M 44
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 11340
63.5%
A 5567
31.2%
e 368
 
2.1%
a 206
 
1.2%
F 162
 
0.9%
m 162
 
0.9%
M 44
 
0.2%

Age
Text

Distinct417
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:25.677389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length51
Median length41
Mean length39.450977
Min length22

Characters and Unicode

Total characters228145
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)4.1%

Sample

1st row18 Years and older   (Adult, Older Adult)
2nd row18 Years and older   (Adult, Older Adult)
3rd row18 Years and older   (Adult, Older Adult)
4th rowChild, Adult, Older Adult
5th row18 Years to 48 Years   (Adult)
ValueCountFrequency (%)
adult 10848
27.0%
older 8586
21.4%
years 7070
17.6%
18 4329
 
10.8%
and 3344
 
8.3%
to 1953
 
4.9%
child 1011
 
2.5%
80 283
 
0.7%
65 240
 
0.6%
75 169
 
0.4%
Other values (96) 2323
 
5.8%
2024-12-09T19:30:26.030529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39670
17.4%
d 23789
 
10.4%
l 20445
 
9.0%
e 15692
 
6.9%
r 15687
 
6.9%
t 12855
 
5.6%
u 10937
 
4.8%
A 10848
 
4.8%
a 10449
 
4.6%
s 7120
 
3.1%
Other values (28) 60653
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
39670
17.4%
d 23789
 
10.4%
l 20445
 
9.0%
e 15692
 
6.9%
r 15687
 
6.9%
t 12855
 
5.6%
u 10937
 
4.8%
A 10848
 
4.8%
a 10449
 
4.6%
s 7120
 
3.1%
Other values (28) 60653
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
39670
17.4%
d 23789
 
10.4%
l 20445
 
9.0%
e 15692
 
6.9%
r 15687
 
6.9%
t 12855
 
5.6%
u 10937
 
4.8%
A 10848
 
4.8%
a 10449
 
4.6%
s 7120
 
3.1%
Other values (28) 60653
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
39670
17.4%
d 23789
 
10.4%
l 20445
 
9.0%
e 15692
 
6.9%
r 15687
 
6.9%
t 12855
 
5.6%
u 10937
 
4.8%
A 10848
 
4.8%
a 10449
 
4.6%
s 7120
 
3.1%
Other values (28) 60653
26.6%

Phases
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.2%
Missing2461
Missing (%)42.6%
Memory size45.3 KiB
Not Applicable
1354 
Phase 2
685 
Phase 3
450 
Phase 1
234 
Phase 2|Phase 3
200 
Other values (3)
399 

Length

Max length15
Median length14
Mean length10.880193
Min length7

Characters and Unicode

Total characters36144
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowPhase 1|Phase 2
3rd rowEarly Phase 1
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable 1354
23.4%
Phase 2 685
 
11.8%
Phase 3 450
 
7.8%
Phase 1 234
 
4.0%
Phase 2|Phase 3 200
 
3.5%
Phase 1|Phase 2 192
 
3.3%
Phase 4 161
 
2.8%
Early Phase 1 46
 
0.8%
(Missing) 2461
42.6%

Length

2024-12-09T19:30:26.211711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T19:30:26.380303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
phase 1968
27.8%
not 1354
19.1%
applicable 1354
19.1%
2 877
12.4%
3 650
 
9.2%
1 280
 
4.0%
2|phase 200
 
2.8%
1|phase 192
 
2.7%
4 161
 
2.3%
early 46
 
0.6%

Most occurring characters

ValueCountFrequency (%)
3760
 
10.4%
a 3760
 
10.4%
e 3714
 
10.3%
l 2754
 
7.6%
p 2708
 
7.5%
s 2360
 
6.5%
h 2360
 
6.5%
P 2360
 
6.5%
b 1354
 
3.7%
o 1354
 
3.7%
Other values (13) 9660
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3760
 
10.4%
a 3760
 
10.4%
e 3714
 
10.3%
l 2754
 
7.6%
p 2708
 
7.5%
s 2360
 
6.5%
h 2360
 
6.5%
P 2360
 
6.5%
b 1354
 
3.7%
o 1354
 
3.7%
Other values (13) 9660
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3760
 
10.4%
a 3760
 
10.4%
e 3714
 
10.3%
l 2754
 
7.6%
p 2708
 
7.5%
s 2360
 
6.5%
h 2360
 
6.5%
P 2360
 
6.5%
b 1354
 
3.7%
o 1354
 
3.7%
Other values (13) 9660
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3760
 
10.4%
a 3760
 
10.4%
e 3714
 
10.3%
l 2754
 
7.6%
p 2708
 
7.5%
s 2360
 
6.5%
h 2360
 
6.5%
P 2360
 
6.5%
b 1354
 
3.7%
o 1354
 
3.7%
Other values (13) 9660
26.7%

Enrollment
Real number (ℝ)

SKEWED  ZEROS 

Distinct962
Distinct (%)16.7%
Missing34
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean18319.489
Minimum0
Maximum20000000
Zeros107
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:26.550874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q160
median170
Q3560
95-th percentile5315.6
Maximum20000000
Range20000000
Interquartile range (IQR)500

Descriptive statistics

Standard deviation404543.73
Coefficient of variation (CV)22.082698
Kurtosis1353.8226
Mean18319.489
Median Absolute Deviation (MAD)135
Skewness34.065934
Sum1.0531874 × 108
Variance1.6365563 × 1011
MonotonicityNot monotonic
2024-12-09T19:30:26.722029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 350
 
6.1%
200 244
 
4.2%
60 176
 
3.0%
30 173
 
3.0%
50 168
 
2.9%
1000 167
 
2.9%
40 166
 
2.9%
300 153
 
2.6%
500 150
 
2.6%
20 128
 
2.2%
Other values (952) 3874
67.0%
ValueCountFrequency (%)
0 107
1.9%
1 8
 
0.1%
2 8
 
0.1%
3 4
 
0.1%
4 5
 
0.1%
5 18
 
0.3%
6 5
 
0.1%
7 6
 
0.1%
8 9
 
0.2%
9 9
 
0.2%
ValueCountFrequency (%)
20000000 1
 
< 0.1%
12000000 1
 
< 0.1%
10000000 2
 
< 0.1%
7882471 1
 
< 0.1%
7000000 1
 
< 0.1%
6000000 2
 
< 0.1%
2000000 1
 
< 0.1%
1302508 1
 
< 0.1%
1200000 1
 
< 0.1%
1000000 5
0.1%

Funded Bys
Categorical

IMBALANCE 

Distinct26
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
Other
4488 
Industry
651 
Other|Industry
 
216
Industry|Other
 
190
Other|NIH
 
97
Other values (21)
 
141

Length

Max length27
Median length5
Mean length6.1647934
Min length3

Characters and Unicode

Total characters35651
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowOther
2nd rowOther
3rd rowOther
4th rowIndustry
5th rowOther

Common Values

ValueCountFrequency (%)
Other 4488
77.6%
Industry 651
 
11.3%
Other|Industry 216
 
3.7%
Industry|Other 190
 
3.3%
Other|NIH 97
 
1.7%
NIH 51
 
0.9%
Other|U.S. Fed 25
 
0.4%
U.S. Fed 15
 
0.3%
Industry|U.S. Fed 10
 
0.2%
NIH|Industry 6
 
0.1%
Other values (16) 34
 
0.6%

Length

2024-12-09T19:30:26.889169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other 4488
76.7%
industry 651
 
11.1%
other|industry 216
 
3.7%
industry|other 190
 
3.2%
other|nih 97
 
1.7%
fed 53
 
0.9%
nih 51
 
0.9%
other|u.s 27
 
0.5%
u.s 20
 
0.3%
industry|u.s 14
 
0.2%
Other values (15) 42
 
0.7%

Most occurring characters

ValueCountFrequency (%)
t 6141
17.2%
r 6141
17.2%
e 5113
14.3%
O 5047
14.2%
h 5047
14.2%
I 1272
 
3.6%
d 1160
 
3.3%
y 1094
 
3.1%
s 1094
 
3.1%
u 1094
 
3.1%
Other values (9) 2448
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 6141
17.2%
r 6141
17.2%
e 5113
14.3%
O 5047
14.2%
h 5047
14.2%
I 1272
 
3.6%
d 1160
 
3.3%
y 1094
 
3.1%
s 1094
 
3.1%
u 1094
 
3.1%
Other values (9) 2448
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 6141
17.2%
r 6141
17.2%
e 5113
14.3%
O 5047
14.2%
h 5047
14.2%
I 1272
 
3.6%
d 1160
 
3.3%
y 1094
 
3.1%
s 1094
 
3.1%
u 1094
 
3.1%
Other values (9) 2448
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 6141
17.2%
r 6141
17.2%
e 5113
14.3%
O 5047
14.2%
h 5047
14.2%
I 1272
 
3.6%
d 1160
 
3.3%
y 1094
 
3.1%
s 1094
 
3.1%
u 1094
 
3.1%
Other values (9) 2448
 
6.9%

Study Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
Interventional
3322 
Observational
2427 
Expanded Access:Intermediate-size Population
 
15
Expanded Access:Treatment IND/Protocol
 
8
Expanded Access:Intermediate-size Population|Treatment IND/Protocol
 
5
Other values (4)
 
6

Length

Max length67
Median length14
Mean length13.764482
Min length13

Characters and Unicode

Total characters79600
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowInterventional
2nd rowInterventional
3rd rowObservational
4th rowObservational
5th rowObservational

Common Values

ValueCountFrequency (%)
Interventional 3322
57.4%
Observational 2427
42.0%
Expanded Access:Intermediate-size Population 15
 
0.3%
Expanded Access:Treatment IND/Protocol 8
 
0.1%
Expanded Access:Intermediate-size Population|Treatment IND/Protocol 5
 
0.1%
Expanded Access:Individual Patients 3
 
0.1%
Expanded Access:Individual Patients|Intermediate-size Population 1
 
< 0.1%
Expanded Access 1
 
< 0.1%
Expanded Access:Individual Patients|Treatment IND/Protocol 1
 
< 0.1%

Length

2024-12-09T19:30:27.007504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-09T19:30:27.136093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
interventional 3322
56.7%
observational 2427
41.4%
expanded 34
 
0.6%
access:intermediate-size 20
 
0.3%
population 16
 
0.3%
ind/protocol 14
 
0.2%
access:treatment 8
 
0.1%
population|treatment 5
 
0.1%
access:individual 5
 
0.1%
patients 3
 
0.1%
Other values (3) 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 12493
15.7%
e 9256
11.6%
t 9186
11.5%
a 8276
10.4%
o 5833
7.3%
i 5827
7.3%
r 5798
7.3%
l 5789
7.3%
v 5754
7.2%
I 3362
 
4.2%
Other values (21) 8026
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 12493
15.7%
e 9256
11.6%
t 9186
11.5%
a 8276
10.4%
o 5833
7.3%
i 5827
7.3%
r 5798
7.3%
l 5789
7.3%
v 5754
7.2%
I 3362
 
4.2%
Other values (21) 8026
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 12493
15.7%
e 9256
11.6%
t 9186
11.5%
a 8276
10.4%
o 5833
7.3%
i 5827
7.3%
r 5798
7.3%
l 5789
7.3%
v 5754
7.2%
I 3362
 
4.2%
Other values (21) 8026
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 12493
15.7%
e 9256
11.6%
t 9186
11.5%
a 8276
10.4%
o 5833
7.3%
i 5827
7.3%
r 5798
7.3%
l 5789
7.3%
v 5754
7.2%
I 3362
 
4.2%
Other values (21) 8026
10.1%
Distinct267
Distinct (%)4.6%
Missing35
Missing (%)0.6%
Memory size45.3 KiB
2024-12-09T19:30:27.369007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length176
Median length168
Mean length100.38309
Min length50

Characters and Unicode

Total characters577002
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)1.8%

Sample

1st rowAllocation: N/A|Intervention Model: Single Group Assignment|Masking: None (Open Label)|Primary Purpose: Diagnostic
2nd rowAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: Triple (Participant, Care Provider, Investigator)|Primary Purpose: Treatment
3rd rowObservational Model: Cohort|Time Perspective: Retrospective
4th rowObservational Model: Cohort|Time Perspective: Retrospective
5th rowObservational Model: Cohort|Time Perspective: Prospective
ValueCountFrequency (%)
model 5748
 
11.7%
assignment|masking 3322
 
6.8%
purpose 3322
 
6.8%
allocation 3319
 
6.8%
randomized|intervention 2459
 
5.0%
observational 2426
 
4.9%
perspective 2426
 
4.9%
parallel 2372
 
4.8%
treatment 2044
 
4.2%
label)|primary 1734
 
3.5%
Other values (46) 19942
40.6%
2024-12-09T19:30:27.751866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 60671
 
10.5%
43366
 
7.5%
n 40055
 
6.9%
o 38596
 
6.7%
i 36295
 
6.3%
r 35705
 
6.2%
t 34246
 
5.9%
a 32675
 
5.7%
s 27475
 
4.8%
l 27150
 
4.7%
Other values (38) 200768
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 577002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 60671
 
10.5%
43366
 
7.5%
n 40055
 
6.9%
o 38596
 
6.7%
i 36295
 
6.3%
r 35705
 
6.2%
t 34246
 
5.9%
a 32675
 
5.7%
s 27475
 
4.8%
l 27150
 
4.7%
Other values (38) 200768
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 577002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 60671
 
10.5%
43366
 
7.5%
n 40055
 
6.9%
o 38596
 
6.7%
i 36295
 
6.3%
r 35705
 
6.2%
t 34246
 
5.9%
a 32675
 
5.7%
s 27475
 
4.8%
l 27150
 
4.7%
Other values (38) 200768
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 577002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 60671
 
10.5%
43366
 
7.5%
n 40055
 
6.9%
o 38596
 
6.7%
i 36295
 
6.3%
r 35705
 
6.2%
t 34246
 
5.9%
a 32675
 
5.7%
s 27475
 
4.8%
l 27150
 
4.7%
Other values (38) 200768
34.8%
Distinct5734
Distinct (%)99.2%
Missing1
Missing (%)< 0.1%
Memory size45.3 KiB
2024-12-09T19:30:28.061547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length90
Median length65
Mean length13.310792
Min length1

Characters and Unicode

Total characters76963
Distinct characters104
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5701 ?
Unique (%)98.6%

Sample

1st rowCOVID-IDNow
2nd rowCOVID19-0001-USR
3rd rowTAC-COVID19
4th rowRC-2020-08
5th rowCHRO-2020-10
ValueCountFrequency (%)
covid-19 120
 
1.7%
covid 77
 
1.1%
2020 52
 
0.7%
46
 
0.6%
in 35
 
0.5%
study 33
 
0.5%
19 22
 
0.3%
covid19 18
 
0.2%
fmasu 17
 
0.2%
irb 15
 
0.2%
Other values (6336) 6835
94.0%
2024-12-09T19:30:28.527373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11971
 
15.6%
2 7462
 
9.7%
1 5340
 
6.9%
- 5074
 
6.6%
C 2814
 
3.7%
3 2337
 
3.0%
I 1997
 
2.6%
9 1970
 
2.6%
4 1779
 
2.3%
5 1756
 
2.3%
Other values (94) 34463
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11971
 
15.6%
2 7462
 
9.7%
1 5340
 
6.9%
- 5074
 
6.6%
C 2814
 
3.7%
3 2337
 
3.0%
I 1997
 
2.6%
9 1970
 
2.6%
4 1779
 
2.3%
5 1756
 
2.3%
Other values (94) 34463
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11971
 
15.6%
2 7462
 
9.7%
1 5340
 
6.9%
- 5074
 
6.6%
C 2814
 
3.7%
3 2337
 
3.0%
I 1997
 
2.6%
9 1970
 
2.6%
4 1779
 
2.3%
5 1756
 
2.3%
Other values (94) 34463
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11971
 
15.6%
2 7462
 
9.7%
1 5340
 
6.9%
- 5074
 
6.6%
C 2814
 
3.7%
3 2337
 
3.0%
I 1997
 
2.6%
9 1970
 
2.6%
4 1779
 
2.3%
5 1756
 
2.3%
Other values (94) 34463
44.8%
Distinct628
Distinct (%)10.9%
Missing34
Missing (%)0.6%
Memory size45.3 KiB
Minimum1998-01-01 00:00:00
Maximum2022-01-01 00:00:00
2024-12-09T19:30:28.874346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:29.048270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct828
Distinct (%)14.4%
Missing36
Missing (%)0.6%
Memory size45.3 KiB
Minimum2016-05-31 00:00:00
Maximum2099-12-31 00:00:00
2024-12-09T19:30:29.233684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:29.442313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct923
Distinct (%)16.1%
Missing36
Missing (%)0.6%
Memory size45.3 KiB
Minimum2018-05-25 00:00:00
Maximum2099-12-31 00:00:00
2024-12-09T19:30:29.628901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:29.817712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct438
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
Minimum2007-12-12 00:00:00
Maximum2021-04-14 00:00:00
2024-12-09T19:30:29.979594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:30.144193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Results First Posted
Date

MISSING 

Distinct33
Distinct (%)91.7%
Missing5747
Missing (%)99.4%
Memory size45.3 KiB
Minimum2019-04-08 00:00:00
Maximum2021-04-14 00:00:00
2024-12-09T19:30:30.286677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:30.438194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
Distinct269
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
Minimum2020-02-05 00:00:00
Maximum2021-04-14 00:00:00
2024-12-09T19:30:30.595049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:30.769224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Locations
Text

MISSING 

Distinct4255
Distinct (%)81.9%
Missing585
Missing (%)10.1%
Memory size45.3 KiB
2024-12-09T19:30:31.140957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length63843
Median length5419
Mean length295.16006
Min length18

Characters and Unicode

Total characters1534242
Distinct characters184
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3765 ?
Unique (%)72.4%

Sample

1st rowGroupe Hospitalier Paris Saint-Joseph, Paris, Ile De France, France
2nd rowCimedical, Barranquilla, Atlantico, Colombia
3rd rowOspedale Papa Giovanni XXIII, Bergamo, Italy|Policlinico San Marco-San Donato group, Bergamo, Italy|Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy|ASST di Lecco Ospedale Alessandro Manzoni, Lecco, Italy|ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle, Melzo, Italy|ASST Monza, Monza, Italy|AUSL Romagna-Ospedale Infermi di Rimini, Rimini, Italy|Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino, San Marino, San Marino
4th rowHong Kong Sanatorium & Hospital, Hong Kong, Hong Kong
5th rowCHR Orléans, Orléans, France
ValueCountFrequency (%)
united 9742
 
5.5%
hospital 5463
 
3.1%
of 3963
 
2.2%
de 3096
 
1.7%
university 3032
 
1.7%
center 2819
 
1.6%
medical 2685
 
1.5%
2492
 
1.4%
site 2443
 
1.4%
research 1880
 
1.1%
Other values (19748) 140645
78.9%
2024-12-09T19:30:31.705697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
173062
 
11.3%
e 127332
 
8.3%
a 122277
 
8.0%
i 111275
 
7.3%
t 95674
 
6.2%
n 92954
 
6.1%
o 71878
 
4.7%
r 68651
 
4.5%
s 66169
 
4.3%
, 65193
 
4.2%
Other values (174) 539777
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1534242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
173062
 
11.3%
e 127332
 
8.3%
a 122277
 
8.0%
i 111275
 
7.3%
t 95674
 
6.2%
n 92954
 
6.1%
o 71878
 
4.7%
r 68651
 
4.5%
s 66169
 
4.3%
, 65193
 
4.2%
Other values (174) 539777
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1534242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
173062
 
11.3%
e 127332
 
8.3%
a 122277
 
8.0%
i 111275
 
7.3%
t 95674
 
6.2%
n 92954
 
6.1%
o 71878
 
4.7%
r 68651
 
4.5%
s 66169
 
4.3%
, 65193
 
4.2%
Other values (174) 539777
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1534242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
173062
 
11.3%
e 127332
 
8.3%
a 122277
 
8.0%
i 111275
 
7.3%
t 95674
 
6.2%
n 92954
 
6.1%
o 71878
 
4.7%
r 68651
 
4.5%
s 66169
 
4.3%
, 65193
 
4.2%
Other values (174) 539777
35.2%

Study Documents
Text

MISSING 

Distinct182
Distinct (%)100.0%
Missing5601
Missing (%)96.9%
Memory size45.3 KiB
2024-12-09T19:30:31.867201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length489
Median length381
Mean length143.82967
Min length85

Characters and Unicode

Total characters26177
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)100.0%

Sample

1st row"Statistical Analysis Plan", https://ClinicalTrials.gov/ProvidedDocs/85/NCT04407585/SAP_000.pdf
2nd row"Study Protocol and Statistical Analysis Plan", https://ClinicalTrials.gov/ProvidedDocs/44/NCT04674644/Prot_SAP_000.pdf
3rd row"Statistical Analysis Plan", https://ClinicalTrials.gov/ProvidedDocs/18/NCT04816318/SAP_000.pdf|"Study Protocol", https://ClinicalTrials.gov/ProvidedDocs/18/NCT04816318/Prot_001.pdf
4th row"Study Protocol and Statistical Analysis Plan", https://ClinicalTrials.gov/ProvidedDocs/96/NCT04474496/Prot_SAP_000.pdf
5th row"Study Protocol and Statistical Analysis Plan", https://ClinicalTrials.gov/ProvidedDocs/63/NCT04713163/Prot_SAP_000.pdf
ValueCountFrequency (%)
protocol 147
11.7%
study 131
10.4%
analysis 124
9.9%
plan 123
9.8%
statistical 104
 
8.3%
and 98
 
7.8%
consent 91
 
7.2%
form 89
 
7.1%
informed 58
 
4.6%
part 4
 
0.3%
Other values (278) 289
23.0%
2024-12-09T19:30:32.188572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1626
 
6.2%
/ 1506
 
5.8%
t 1426
 
5.4%
i 1402
 
5.4%
l 1278
 
4.9%
s 1222
 
4.7%
a 1121
 
4.3%
d 1095
 
4.2%
0 1085
 
4.1%
1076
 
4.1%
Other values (54) 13340
51.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1626
 
6.2%
/ 1506
 
5.8%
t 1426
 
5.4%
i 1402
 
5.4%
l 1278
 
4.9%
s 1222
 
4.7%
a 1121
 
4.3%
d 1095
 
4.2%
0 1085
 
4.1%
1076
 
4.1%
Other values (54) 13340
51.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1626
 
6.2%
/ 1506
 
5.8%
t 1426
 
5.4%
i 1402
 
5.4%
l 1278
 
4.9%
s 1222
 
4.7%
a 1121
 
4.3%
d 1095
 
4.2%
0 1085
 
4.1%
1076
 
4.1%
Other values (54) 13340
51.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1626
 
6.2%
/ 1506
 
5.8%
t 1426
 
5.4%
i 1402
 
5.4%
l 1278
 
4.9%
s 1222
 
4.7%
a 1121
 
4.3%
d 1095
 
4.2%
0 1085
 
4.1%
1076
 
4.1%
Other values (54) 13340
51.0%

URL
Text

UNIQUE 

Distinct5783
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2024-12-09T19:30:32.413963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length43
Median length43
Mean length43
Min length43

Characters and Unicode

Total characters248669
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5783 ?
Unique (%)100.0%

Sample

1st rowhttps://ClinicalTrials.gov/show/NCT04785898
2nd rowhttps://ClinicalTrials.gov/show/NCT04595136
3rd rowhttps://ClinicalTrials.gov/show/NCT04395482
4th rowhttps://ClinicalTrials.gov/show/NCT04416061
5th rowhttps://ClinicalTrials.gov/show/NCT04395924
ValueCountFrequency (%)
https://clinicaltrials.gov/show/nct04785898 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04473170 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04416061 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04395924 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04516954 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04476940 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04634214 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04602884 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04384588 1
 
< 0.1%
https://clinicaltrials.gov/show/nct04355897 1
 
< 0.1%
Other values (5773) 5773
99.8%
2024-12-09T19:30:32.773042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 23132
 
9.3%
s 17349
 
7.0%
l 17349
 
7.0%
i 17349
 
7.0%
h 11566
 
4.7%
t 11566
 
4.7%
C 11566
 
4.7%
a 11566
 
4.7%
T 11566
 
4.7%
o 11566
 
4.7%
Other values (20) 104094
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 248669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 23132
 
9.3%
s 17349
 
7.0%
l 17349
 
7.0%
i 17349
 
7.0%
h 11566
 
4.7%
t 11566
 
4.7%
C 11566
 
4.7%
a 11566
 
4.7%
T 11566
 
4.7%
o 11566
 
4.7%
Other values (20) 104094
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 248669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 23132
 
9.3%
s 17349
 
7.0%
l 17349
 
7.0%
i 17349
 
7.0%
h 11566
 
4.7%
t 11566
 
4.7%
C 11566
 
4.7%
a 11566
 
4.7%
T 11566
 
4.7%
o 11566
 
4.7%
Other values (20) 104094
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 248669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 23132
 
9.3%
s 17349
 
7.0%
l 17349
 
7.0%
i 17349
 
7.0%
h 11566
 
4.7%
t 11566
 
4.7%
C 11566
 
4.7%
a 11566
 
4.7%
T 11566
 
4.7%
o 11566
 
4.7%
Other values (20) 104094
41.9%

Interactions

2024-12-09T19:30:17.267978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:17.007653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:17.387268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-09T19:30:17.143436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-09T19:30:32.879841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
EnrollmentFunded BysGenderPhasesRankStatusStudy ResultsStudy Type
Enrollment1.0000.0000.0000.000-0.1120.0000.0000.030
Funded Bys0.0001.0000.0560.1550.0600.0460.0620.099
Gender0.0000.0561.0000.1260.0780.0140.0660.000
Phases0.0000.1550.1261.0000.1420.0750.0601.000
Rank-0.1120.0600.0780.1421.0000.0400.0000.059
Status0.0000.0460.0140.0750.0401.0000.1580.419
Study Results0.0000.0620.0660.0600.0000.1581.0000.018
Study Type0.0300.0990.0001.0000.0590.4190.0181.000

Missing values

2024-12-09T19:30:17.632547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-09T19:30:18.081851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-09T19:30:18.469675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RankNCT NumberTitleAcronymStatusStudy ResultsConditionsInterventionsOutcome MeasuresSponsor/CollaboratorsGenderAgePhasesEnrollmentFunded BysStudy TypeStudy DesignsOther IDsStart DatePrimary Completion DateCompletion DateFirst PostedResults First PostedLast Update PostedLocationsStudy DocumentsURL
01NCT04785898Diagnostic Performance of the ID Now™ COVID-19 Screening Test Versus Simplexa™ COVID-19 Direct AssayCOVID-IDNowActive, not recruitingNo Results AvailableCovid19Diagnostic Test: ID Now™ COVID-19 Screening TestEvaluate the diagnostic performance of the ID Now ™ COVID-19 test carried out by nurses in an emergency department in comparison with the reference PCR test: Simplexa ™ COVID-19 DirectGroupe Hospitalier Paris Saint JosephAll18 Years and older   (Adult, Older Adult)Not Applicable1000.0OtherInterventionalAllocation: N/A|Intervention Model: Single Group Assignment|Masking: None (Open Label)|Primary Purpose: DiagnosticCOVID-IDNowNovember 9, 2020December 22, 2020April 30, 2021March 8, 2021NaNMarch 8, 2021Groupe Hospitalier Paris Saint-Joseph, Paris, Ile De France, FranceNaNhttps://ClinicalTrials.gov/show/NCT04785898
12NCT04595136Study to Evaluate the Efficacy of COVID19-0001-USR in Patients With Mild/or Moderate COVID-19 Infection in OutpatientCOVID-19Not yet recruitingNo Results AvailableSARS-CoV-2 InfectionDrug: Drug COVID19-0001-USR|Drug: normal salineChange on viral load results from baseline after using COVID19-0001-USR via nebulizationUnited Medical SpecialtiesAll18 Years and older   (Adult, Older Adult)Phase 1|Phase 260.0OtherInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: Triple (Participant, Care Provider, Investigator)|Primary Purpose: TreatmentCOVID19-0001-USRNovember 2, 2020December 15, 2020January 29, 2021October 20, 2020NaNOctober 20, 2020Cimedical, Barranquilla, Atlantico, ColombiaNaNhttps://ClinicalTrials.gov/show/NCT04595136
23NCT04395482Lung CT Scan Analysis of SARS-CoV2 Induced Lung InjuryTAC-COVID19RecruitingNo Results Availablecovid19Other: Lung CT scan analysis in COVID-19 patientsA qualitative analysis of parenchymal lung damage induced by COVID-19|A quantitative analysis of parenchymal lung damage induced by COVID-19|The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.|Automated segmentation of lung scans of patients with COVID-19 and ARDS.|Knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques.|The ability within which the analysis of artificial intelligence that uses deep learning models can be used to predict clinical outcomesUniversity of Milano BicoccaAll18 Years and older   (Adult, Older Adult)NaN500.0OtherObservationalObservational Model: Cohort|Time Perspective: RetrospectiveTAC-COVID19May 7, 2020June 15, 2021June 15, 2021May 20, 2020NaNNovember 9, 2020Ospedale Papa Giovanni XXIII, Bergamo, Italy|Policlinico San Marco-San Donato group, Bergamo, Italy|Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy|ASST di Lecco Ospedale Alessandro Manzoni, Lecco, Italy|ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle, Melzo, Italy|ASST Monza, Monza, Italy|AUSL Romagna-Ospedale Infermi di Rimini, Rimini, Italy|Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino, San Marino, San MarinoNaNhttps://ClinicalTrials.gov/show/NCT04395482
34NCT04416061The Role of a Private Hospital in Hong Kong Amid COVID-19 PandemicCOVID-19Active, not recruitingNo Results AvailableCOVIDDiagnostic Test: COVID 19 Diagnostic TestProportion of asymptomatic subjects|Proportion of subjects with recent contact history|Proportion of subjects with recent travel historyHong Kong Sanatorium & HospitalAllChild, Adult, Older AdultNaN2500.0IndustryObservationalObservational Model: Cohort|Time Perspective: RetrospectiveRC-2020-08May 25, 2020July 31, 2020August 31, 2020June 4, 2020NaNJune 4, 2020Hong Kong Sanatorium & Hospital, Hong Kong, Hong KongNaNhttps://ClinicalTrials.gov/show/NCT04416061
45NCT04395924Maternal-foetal Transmission of SARS-Cov-2TMF-COVID-19RecruitingNo Results AvailableMaternal Fetal Infection Transmission|COVID-19|SARS-CoV 2Diagnostic Test: Diagnosis of SARS-Cov2 by RT-PCR and : IgG, Ig M serologies in the amniotoc fluid, the blood cord and the placentaCOVID-19 by positive PCR in cord blood and / or positive serologies|COVID-19 by positive PCR in amniotic fluid and placenta|New born infected by COVID-19Centre Hospitalier Régional d'Orléans|Centre de Biophysique Moléculaire - Pr Chantal Pichon|Professeur TOUMI HechmiFemale18 Years to 48 Years   (Adult)NaN50.0OtherObservationalObservational Model: Cohort|Time Perspective: ProspectiveCHRO-2020-10May 5, 2020May 2021May 2021May 20, 2020NaNJune 4, 2020CHR Orléans, Orléans, FranceNaNhttps://ClinicalTrials.gov/show/NCT04395924
56NCT04516954Convalescent Plasma for COVID-19 PatientsCPCPEnrolling by invitationNo Results AvailableCOVID 19Biological: Convalescent COVID 19 PlasmaEvaluate the safety|Change in requirement for mechanical ventilatory supportVinmec Research Institute of Stem Cell and Gene Technology|National Institute of Hygiene and Epidemiology, Vietnam|National Hospital for Tropical Diseases, Hanoi, Vietnam|National Institute of Hematology and Blood Transfusion, VietnamAll18 Years to 75 Years   (Adult, Older Adult)Early Phase 110.0OtherInterventionalAllocation: N/A|Intervention Model: Single Group Assignment|Masking: None (Open Label)|Primary Purpose: TreatmentISC.20.11.1August 1, 2020November 30, 2020December 30, 2020August 18, 2020NaNAugust 20, 2020Vinmec Research Institute of Stem cell and Gene Technology, Hanoi, VietnamNaNhttps://ClinicalTrials.gov/show/NCT04516954
67NCT04476940COVID-19 Breastfeeding Guideline for African-AmericansCOVID-BFNot yet recruitingNo Results AvailableCovid19|Exclusive BreastfeedingBehavioral: COVID-19 Breastfeeding SupportCOVID-19 breastfeeding guidance adherence at birth.|COVID-19 breastfeeding guidance adherence at 1-month postpartum.|COVID-19 breastfeeding guidance adherence at 3-months postpartum.|Exclusive breastfeeding at birth.|Exclusive breastfeeding at 1-month.|Exclusive breastfeeding at 3-months.|COVID_Status|COVID-19 Immunoglobulin G|COVID-19 Immunoglobulin MMeharry Medical CollegeFemale18 Years to 45 Years   (Adult)Not Applicable200.0OtherInterventionalAllocation: N/A|Intervention Model: Single Group Assignment|Masking: None (Open Label)|Primary Purpose: Prevention330875September 2020October 2021June 2022July 20, 2020NaNJuly 20, 2020Meharry Medical College, Nashville, Tennessee, United StatesNaNhttps://ClinicalTrials.gov/show/NCT04476940
78NCT04634214The Severity of COVID 19 in Diabetes and Non-diabetes PatientsCOVID19Not yet recruitingNo Results AvailableCovid19|Type2 DiabetesNaNSeverity of COVID 19 among people with and without diabetes|Number of patients who were in ICU|Number of patients who had tracheal intubation|Number of patients who had respiratory complication|Number of death|Correlation of BMI with complications, tracheal intubation and mortality|Length of hospital stayIndia Diabetes Research Foundation & Dr. A. Ramachandran's Diabetes HospitalsAll18 Years to 99 Years   (Adult, Older Adult)NaN1050.0OtherObservationalObservational Model: Case-Control|Time Perspective: RetrospectiveIDRFARH015November 16, 2020February 16, 2021May 16, 2021November 18, 2020NaNNovember 18, 2020Orthomed Hospital, Chennai, Tamil Nadu, India|Medway Hospital, Chennai, Tamil Nadu, India|Venkateswara Hospital, Chennai, Tamil Nadu, India|Dr. Rela Institute & Medical Center, Chennai, Tamil Nadu, IndiaNaNhttps://ClinicalTrials.gov/show/NCT04634214
89NCT04602884Early Detection of COVID-19 Using Breath AnalysisCOVID-19SuspendedNo Results AvailableCovid19Diagnostic Test: Breath biopsy sampling using the ReCIVA Breath SamplerCorrelation between Volatile Organic Compounds pattern and COVID-19 detection status.|Correlation between Volatile Organic Compounds pattern and time from COVID-19 detection.|Correlation between the set of Volatile Organic Compounds found in breath biopsy and disease intensity.Scentech Medical Technologies LtdAll18 Years to 55 Years   (Adult)Not Applicable50.0IndustryInterventionalAllocation: Non-Randomized|Intervention Model: Parallel Assignment|Masking: None (Open Label)|Primary Purpose: DiagnosticCov-2-IDFSeptember 22, 2020December 30, 2021December 30, 2021October 26, 2020NaNApril 13, 2021IDF COVID 19 Isolation Facility, Ashkelon, IsraelNaNhttps://ClinicalTrials.gov/show/NCT04602884
910NCT04384588COVID19-Convalescent Plasma for Treating Patients With Active Symptomatic COVID 19 Infection (FALP-COVID)FALP-COVIDRecruitingNo Results AvailableCOVID-19 Infection|Cancer Patients|General PopulationBiological: Convalescent Plasma from COVID-19 donorsin-hospital mortality secondary to COVID-19 among patients treated with convalescent plasma|safety of the use of convalescent plasma drom COVID 19 donors|Mortality at 30 days, 90 days, 6 months and 1 year|in-hospital Mortality COVID-19 related compared with non-treated population according to Chilean official reports|Number of days of hospitalization in high complexity facilities after convalescent plasma use|Number of days of hospitalization in intensive care unit after convalescent plasma use|Number of days of mechanical ventilatory support in patients after convalescent plasma use|Total number of days of mechanical ventilatory support|Total number of hospitalization days in patients treated with convalescent plasma|Number of hospitalization days in patients after treatment with convalescent plasma|Viral load measuring|Immunological response in treated patients (COVID19-Immunoglobulin M and Immunoglobulin G, neutralizing antibodies)|Negativization of COVID 19 load since convalescent plasma use|Negativization of COVID 19 load since hospitalization|Negativization of COVID 19 load since first reported symptoms COVID-19 related|Donor Interferon Gamma profile characterization|Donor Granulocyte Macrophage Colony Stimulating Factor characterization|Donor Tumor Necrosis Factor Alfa characterization|Donor Interleukin -1 beta characterization|Donor Interleukin-2 characterization|Donor Interleukin-4 characterization|Donor Interleukin-6 characterization|Donor Interleukin-8 characterization|Donor Interleukin-10 characterization|Receptor Interferon Gamma profile characterization|Receptor Granulocyte Macrophage Colony Stimulating Factor characterization|receptor Tumor Necrosis Factor Alfa characterization|receptor Interleukin -1 beta characterization|Receptor Interleukin-2 characterization|Receptor Interleukin-4 characterization|Receptor Interleukin-6 characterization|Receptor Interleukin-8 characterization|Receptor Interleukin-10 characterizationFundacion Arturo Lopez Perez|Confederación de la Producción y del Comercio (CPC)|Bolsa de SantiagoAll15 Years and older   (Child, Adult, Older Adult)Phase 2|Phase 3100.0OtherInterventionalAllocation: Non-Randomized|Intervention Model: Parallel Assignment|Masking: None (Open Label)|Primary Purpose: TreatmentFALP 001-2020April 7, 2020April 6, 2021April 6, 2021May 12, 2020NaNMay 12, 2020Fundacion Arturo Lopez Perez, Providencia, Santiago, ChileNaNhttps://ClinicalTrials.gov/show/NCT04384588
RankNCT NumberTitleAcronymStatusStudy ResultsConditionsInterventionsOutcome MeasuresSponsor/CollaboratorsGenderAgePhasesEnrollmentFunded BysStudy TypeStudy DesignsOther IDsStart DatePrimary Completion DateCompletion DateFirst PostedResults First PostedLast Update PostedLocationsStudy DocumentsURL
57735774NCT04639661Predictors of Periodontal Outcomes Post-sanative TherapyNaNEnrolling by invitationNo Results AvailablePeriodontal Diseases|Periodontal PocketNaNProbing depth|Bleeding on probing|Tooth Loss|O'Leary Index of Plaque ControlBrock University|Dr. Peter C. Fritz, Periodontal Wellness & Implant SurgeryAll19 Years and older   (Adult, Older Adult)NaN200.0OtherObservationalObservational Model: Cohort|Time Perspective: Retrospective20-070November 25, 2020August 2021December 2021November 20, 2020NaNDecember 4, 2020Dr. Peter C. Fritz, Periodontal Wellness & Implant Surgery, Fonthill, Ontario, Canada|Brock University, St. Catharines, Ontario, CanadaNaNhttps://ClinicalTrials.gov/show/NCT04639661
57745775NCT04180709CBT to Reduce Insomnia and Improve Social Recovery in Early PsychosisCRISPRecruitingNo Results AvailablePsychotic Disorders|Psychosis|SleepDevice: SleepioChange from baseline Work and Social Adjustment Scale (WSAS) score at week 9 of study|Time Use Survey - Structured Hours (TUS-SH)|Patient Health Questionnaire (PHQ-9)|Rapid Visual Information Processing (RVP) / CANTAB Cognitive Test|Paired Associates Learning (PAL) / CANTAB Cognitive Test|Spatial Working Memory (SWM) / CANTAB Cognitive Test|Emotion Recognition Task (ERT) / CANTAB Cognitive Test|Global Assessment of Functioning (GAF) (split version / subscales GAF-D & GAF-S)|Change from baseline Work and Social Adjustment Scale (WSAS) score at week 17|Change from baseline Work and Social Adjustment Scale (WSAS) score at week 5, to correct for confounding in mediation analysis|Change from baseline Work and Social Adjustment Scale (WSAS) score at week 13, to correct for confounding in mediation analysisUniversity of Cambridge|Cambridgeshire and Peterborough NHS Foundation Trust|Cambridge Cognition Ltd|Big HealthAll18 Years and older   (Adult, Older Adult)Not Applicable44.0Other|IndustryInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: None (Open Label)|Primary Purpose: Supportive CareM00915|RNAG-521|224101|19/EE/0352October 30, 2020November 30, 2022November 30, 2022November 27, 2019NaNNovember 3, 2020Cameo Early Intervention, Cambridge, Cambridgeshire, United KingdomNaNhttps://ClinicalTrials.gov/show/NCT04180709
57755776NCT04335643Telehealth CBT for Adolescents and Young Adults With Childhood-onset Systemic Lupus ErythematosuscSLERecruitingNo Results AvailableSystemic Lupus ErythematosusBehavioral: TEACHRecruitment rates of the study|Retention rates of the study|Feasibility of remotely-delivered TEACH|Changes in fatigue, as measured by the PROMIS Fatigue SF|Long-term changes in fatigue, as measured by the PROMIS Fatigue SF|Changes in depressive symptoms, as measured by the CDI-2 and BDI-II|Long-term changes in depressive symptoms, as measured by the CDI-2 and BDI-IIMichigan State University|Arthritis Foundation|The Hospital for Sick ChildrenAll12 Years to 22 Years   (Child, Adult)Not Applicable75.0OtherInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: None (Open Label)|Primary Purpose: TreatmentSTUDY00003882August 4, 2020December 2021December 2021April 6, 2020NaNMarch 9, 2021Michigan State University, Grand Rapids, Michigan, United States|The Hospital for Sick Children, Toronto, Ontario, CanadaNaNhttps://ClinicalTrials.gov/show/NCT04335643
57765777NCT04589377Mindfulness to Mitigate Psychological Threat and Improve Engagement and Learning in Introductory Physics CoursesNaNRecruitingNo Results AvailableStressBehavioral: Mindfulness TrainingMean differences in Psychological Threat measured via Ecological Momentary Assessment|Mean differences in Physics Motivation assessed via Ecological Momentary Assessment|Mean change in Psychological Threat measured via Self-Report|Mean change in Physics Interest measured via Self-Report|Mean change in Physics Self-Efficacy measured via Self-Report|Mean change in Physics Value measured via Self-Report|Mean change in Physics Identity measured via Self-Report|Mean change in Physics Belonging measured via Self-Report|Mean change in Physics Achievement Goals measured via Self-Report|Mean change in Physics Growth Mindset measured via Self-Report|Mean differences in Meaning Making and Positive Reappraisal assessed via Ecological Momentary Assessment|Mean differences in Affect assessed via Ecological Momentary Assessment|Mean differences in Mindfulness assessed via Ecological Momentary Assessment|Mean differences in Equanimity assessed via Ecological Momentary Assessment|Mean change in Meaning Making and Positive Reappraisal assessed via Self-Report|Mean change in Physics Engagement assessed via Self-Report|Mean change in Physics Anxiety assessed via Self-Report|Mean change in Physics performance on the assessment|Mean differences in Performance on the Preparation for Future Learning Task|Mean change of judgments of confidence, anxiety, and difficulty during physics assessment via Self-ReportUniversity of Pittsburgh|U.S. National Science Foundation|James S McDonnell FoundationAll18 Years and older   (Adult, Older Adult)Not Applicable150.0Other|U.S. FedInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: Single (Participant)|Primary Purpose: TreatmentSTUDY19050258October 26, 2020August 31, 2022December 31, 2022October 19, 2020NaNNovember 10, 2020University of Pittsburgh, Pittsburgh, Pennsylvania, United StatesNaNhttps://ClinicalTrials.gov/show/NCT04589377
57775778NCT04574466Scaling-up Psychological Interventions With Syrian Refugees in Switzerland (STRENGTHS_CH): RCTNaNRecruitingNo Results AvailableDistress|PTSD|Anxiety|Depression|Trauma|Functional Disabilities|Common Mental Health ProblemsBehavioral: Problem Management PlusChange in psychological distress|Change in posttraumatic stress disorder symptoms|Change in functional disabilityUniversity of ZurichAll18 Years and older   (Adult, Older Adult)Not Applicable380.0OtherInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: Single (Outcomes Assessor)|Primary Purpose: TreatmentBASEC-2017-01175-rctAugust 25, 2020June 2022June 2022October 5, 2020NaNOctober 5, 2020Klinik für Konsiliarpsychiatrie und Psychosomatik, Zürich, SwitzerlandNaNhttps://ClinicalTrials.gov/show/NCT04574466
57785779NCT04011644Mobile Health for Alcohol Use Disorders in Clinical PracticeNaNRecruitingNo Results AvailableAlcohol Drinking|TelemedicineBehavioral: A-CHESS self-monitored|Behavioral: A-CHESS peer-supported|Behavioral: A-CHESS clinically-integratedNumber of risky drinking days|Number of patients who are willing to share data|Number of healthcare services used in the past 6 months|Cost of implementation of each intervention arm in US dollars|Number of patient risk factors assessed by a revised Brief Alcohol Monitor|Number of days health coach/other clinician used the A-CHESS dashboard|Score of Alcohol Use Disorders Identification Test (AUDIT) screening tool|Score of the Diagnostic and Statistical Manual- 5 Alcohol use disorder (AUD) severity|Score of relatedness as assessed by the CHESS Bonding Scale|Score of competence as assessed by the Perceived competence scale (PCS)|Score of autonomous motivation as assessed by revised Treatment Self Regulation Questionnaire|Time of A-CHESS used|Number of setback status triggered by A-CHESS|Variables used for predictive setback status|Number of response statuses addressed|Score of overall quality of life as assessed by Patient Reported Outcomes Measurement Information System (PROMIS) Global-10|Pages viewed on A-CHESS|Number of patient protection factors assessed by a revised Brief Alcohol MonitorUniversity of Wisconsin, Madison|National Institute on Alcohol Abuse and Alcoholism (NIAAA)All21 Years to 100 Years   (Adult, Older Adult)Not Applicable566.0Other|NIHInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: None (Open Label)|Primary Purpose: Treatment2019-0337|R01AA024150|A532007|SMPH/FAMILY MED/RES GRANTS|Protocol Version 1/26/2021March 23, 2020August 2022April 2023July 8, 2019NaNApril 2, 2021UW Health at the American Center, Madison, Wisconsin, United StatesNaNhttps://ClinicalTrials.gov/show/NCT04011644
57795780NCT04681339Antibiotic Prescription in Children Hospitalized for Community-acquired PneumoniaNaNNot yet recruitingNo Results AvailableCommunity Acquired Pneumonia in Children|Antibiotic StewardshipOther: Antibiotic treatment|Other: No antibiotic treatmentAntibiotic treatment rates in hospitalized children with non-severe community-acquired pneumonia and fever|Number of medical complications|Factors in physician decision making on antibiotic prescription|Parental satisfaction|Hospitalization duration|Number of children with relevant comorbidity|Days of supplemental oxygen use|Use of antipyretic medications|Number of complementary medicine medications used per child|Number of readmissions for pneumonia or new pneumonia recurrences within 4 weeks of hospital dischargeARCIM Institute Academic Research in Complementary and Integrative Medicine|Gemeinschaftskrankenhaus Herdecke, GermanyAll3 Months to 18 Years   (Child, Adult)NaN200.0OtherObservationalObservational Model: Cohort|Time Perspective: ProspectivePKA-03April 2021November 2024December 2024December 23, 2020NaNFebruary 10, 2021Die Filderklinik, Filderstadt, Baden-Württemberg, Germany|Herdecke Community Hospital, Herdecke, Nordrhein-Westfalen, GermanyNaNhttps://ClinicalTrials.gov/show/NCT04681339
57805781NCT04740229Moderate-intensity Flow-based Yoga Effects on Cognition and StressNaNRecruitingNo Results AvailableStress|PsychologicalBehavioral: YogaPerceived Stress|Task switching paradigm|Digit span forward and backward|Digit symbol substitution test (DSST)|Stroop taskUniversity of Illinois at Urbana-ChampaignAll18 Years to 64 Years   (Adult)Not Applicable88.0OtherInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: Double (Investigator, Outcomes Assessor)|Primary Purpose: Treatment21584February 10, 2021July 2021July 2021February 5, 2021NaNFebruary 24, 2021University of Illinois at Urbana-Champaign, Urbana, Illinois, United StatesNaNhttps://ClinicalTrials.gov/show/NCT04740229
57815782NCT048049173-year Follow-up of the Mind My Mind RCTMindMyMindFURecruitingNo Results AvailableEmotional Problem|Anxiety Disorder of Childhood|Depressive Symptoms|Behavior Problem of Childhood and Adolescence|Mental Health DisorderNaNThe child's impact of mental health problems reported by the parent on the Strengths and Difficulties Questionnaire Impact Scale.|Emotional and behavioral problems|School attendance: proportion of school-days within the last 4 weeks, where the child is present (no illegal absence)|Child Health Utility instrument (CHU9D)|The Parental Stress Scale (PSS)Mental Health Services in the Capital Region, Denmark|Frederiksberg University Hospital|Defactum, Central Denmark Region|The Danish Mental Health FoundationAll8 Years to 20 Years   (Child, Adult)NaN396.0OtherObservationalObservational Model: Other|Time Perspective: ProspectiveMHSCRDenmark, F-61502-03-1March 22, 2021May 31, 2022December 31, 2022March 18, 2021NaNApril 1, 2021Mental Health Services in the Capital Region, Denmark, Copenhagen, DenmarkNaNhttps://ClinicalTrials.gov/show/NCT04804917
57825783NCT04680000Chronic Pain Management In Primary Care Using Behavioral Health ConsultantsNaNNot yet recruitingNo Results AvailableChronic PainBehavioral: Brief Cognitive Behavioral Therapy for Chronic Pain (BCBT-CP)|Other: BCBT-CP BoosterDefense and Veterans Pain Rating Scale (DVPRS)|Behavioral Health Measure-20 (BHM-20)|Pain Intensity, Enjoyment and General Activity (PEG-3)|Modified Oswestry Disability Index (ODI)|Pain Catastrophizing Scale (PCS)|Chronic Pain Acceptance Questionnaire (CPAQ)|Insomnia Severity Index (ISI)|Two-Item Patient Health Questionnaire (PHQ-2)The University of Texas Health Science Center at San Antonio|Uniformed Services University of the Health Sciences|Massachusetts General Hospital|Defense Health Agency|59th Medical Wing|Brooke Army Medical Center|C.R.Darnall Army Medical CenterAll18 Years and older   (Adult, Older Adult)Not Applicable716.0Other|U.S. FedInterventionalAllocation: Randomized|Intervention Model: Parallel Assignment|Masking: None (Open Label)|Primary Purpose: TreatmentHSC20200520HFebruary 2021February 2024February 2025December 22, 2020NaNDecember 22, 2020Uniformed Services University for the Health Sciences, Bethesda, Maryland, United States|University of Texas Health Science Center San Antonio, San Antonio, Texas, United StatesNaNhttps://ClinicalTrials.gov/show/NCT04680000